6) Scene15 consists of 4485 images from 15 indoor and outdoor scene categories. Three types features (GIST, PHOG and LBP) are extracted as multiple views.
Comparison with CCA-based algorithms
We compared our method with the CCA-based algorithms. Specifically, we employ CCA-based methods to obtain the latent representations and then the linear SVM classifier is used for classification. The following CCA-based algorithms are used as baselines. (1) CCA [1]} is a classical algorithm for seeking the correlations between multiple types of features. (2) DCCA [2]} obtains the correlations through deep neural networks. (3) DCCAE [3]} employs autoencoders for seeking the common representations. (4) BCCA [4]} presents a Bayesian model selection algorithm for CCA based on a probabilistic interpretation. Due to randomness (e.g., training/testing partition and optimization) involved, for each method, we run 30 times and report its mean and standard deviation in terms of classification accuracy. The experimental results are shown in Table REF . On all datasets, our method consistently achieves better performance compared with these CCA-based algorithms. Note that our method is quite different from the CCA-based methods. Ours is a classification model while most CCA-based methods are unsupervised representation learning models. Meanwhile, to the best of our knowledge, existing CCA-based algorithms are unable to provide trusted decisions.
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Experimental analysis of removing view
<TABLE>We conduct experiments by removing one view manually on two datasets (i.e., PIE and Scene15) associated with three views. We run 30 times and report the mean and standard deviation in terms of classification accuracy. The experimental results are shown in Table REF . It is observed that by using more information, the performance of our model tends to be improved.

Details of the implementation
For our trusted multi-view classification (TMC) algorithm, we employ the fully connected networks for all datasets, and \(l_2\) -norm regularization is used with the value of the trade-off parameter being 0.0001. The Adam [5]} optimizer is used to train the network. 5-fold cross-validation is employed to select the learning rate from \(\lbrace 3e^{-3}, 1e^{-3}, 3e^{-4}, 1e^{-4}\rbrace \) . For all datasets, \(20\%\)  of samples are used as test
set. The hyperparameter \(\lambda _t\)  in
Eq. REF  slowly increases from 0 to 1. For all methods, we run 30 times to report the mean values and standard deviations.

End-to-end experiment
Although the effectiveness has been well validated on datasets with multiple types of features, we further conduct experiments in an end-to-end manner on multi-modal datasets. The UMPC-FOOD101 dataset consists of 86,796 samples scraped from web pages, each sample is described with both image and text [6]}. Similarly to existing work [7]}, there are 60,101 samples used as the training set, 5,000 samples used as the validation set, and the remaining 21,695 samples are used as the test set. The samples of NYUD (RGB-D) dataset are collected by cropping out tight bounding boxes around instances of 19 object classes in the NYUD [8]} dataset. There are 4,587 samples, and each sample is composed of both RGB and depth modalities. We use 2,186, 1,200, and 1,201 samples as the training set, validation set, and test set respectively. In this experiment, we used the original datasets which contain noise due to the collection process.
For UMPC-FOOD101, we employ ResNet-152 [9]} pre-trained with ImageNet and BERT [10]} as the backbone network for image and text respectively. For NYUD (RGB-D), ResNet-50 [9]} pre-trained with ImageNet is used as the backbone network for depth and RGB images. For comparison algorithms, we report the results of the best performing views, and furthermore, we concatenate the outputs of the two backbone networks as the input of the classifier as shown in Fig. REF . For all algorithms, due to the randomness involved, we run 10 times to report the mean accuracy and standard deviation. The Adam [5]} optimizer is employed for all methods with learning rate decay.
Quantitative experimental results. The experimental results on the above datasets are shown in Table REF . We report the accuracy of each compared algorithm with the best-performing view (`best view' in Table REF ) and combined views (`feature fusion' in Table REF ). According to Table REF , our algorithm outperforms other methods on all datasets. For example, on the Food101 dataset, our method achieved \(91.3\%\)  in terms of accuracy, compared with \(90.5\%\)  from the second performer. The performance of using the best single view is relatively low.
<FIGURE><TABLE>Qualitative experimental results.
We also present typical examples (from UMPC-Food101) with the highest and lowest uncertainty in prediction in Table REF  and Table REF , respectively. Since the UMPC-Food101 dataset is scraped from web pages, for a few samples the corresponding text descriptions are meaningless (e.g., the 1st one in Table REF ). At the same time, some images are also quite challenging to classify (e.g., the 2nd and the 4th in Table REF ). For these samples that can be considered as out-of-distribution ones, it is difficult to obtain evidence related to classification. As expected, these samples are assigned with high uncertainty (top 5) by our algorithm. In contrast, Table REF  presents the top 5 samples with lowest uncertainty. We can find that for these samples the labels are usually explicitly mentioned in the text and the images also clearly show the characteristics of `macarons'.
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